BackgroundQuality assurance (QA) of patient‐specific treatment plans for intensity‐modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks.PurposeThe primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations.MethodWe devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators.ResultsThe accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors.ConclusionWhen juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

中文翻译：

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基于深度学习网络的放疗计划验证错误检测

背景调强放射治疗 (IMRT) 和容积调制弧形治疗 (VMAT) 的患者特定治疗计划的质量保证 (QA) 需要事先验证。然而，标准方法在位置剂量分布数据分析方面存在缺陷和缺乏敏感性，导致难以准确识别计划验证失败的原因。这个问题使 QA 任务变得复杂并阻碍了 QA 任务的效率。目的本研究的主要目的是利用深度学习算法提取 3D 剂量分布图，并创建跨多个机器模型、治疗方法和方法的错误分类的预测模型。方法我们设计了五类验证计划（正常、龙门误差、准直器误差、治疗床误差和剂量误差），符合不同精度水平的耐受限度，并采用来自 94 名肿瘤患者样本的 3D 剂量分布数据。然后构建 CNN 模型来预测不同的错误类型，并使用不同的阈值（3%, 3 mm；3%, 2 mm；2%, 2 mm）将预测结果与伽玛通过率 (GPR) 标准进行比较，以评估错误类型模型的性能。此外，我们通过评估模型在不同加速器上的功能来评估模型的鲁棒性。结果CNN 模型性能的准确度、精确度、召回率和 F1 分数分别为 0.907、0.925、0.907 和 0.908。同时，另一台设备上的性能为 0.900、0.918、0.900 和 0.898。此外，与GPR方法相比，CNN模型在预测不同类型的错误方面取得了更好的结果。结论当与GPR方法并列时，CNN模型在不同设备上验证放射治疗计划时表现出优越的分类预测能力。通过使用该模型，可以更快速、更有效地检测计划验证失败，从而最大限度地减少 QA 任务所需的时间，并作为克服 GPR 方法的限制的有价值的辅助手段。